Pregnancy History and Disease Progression Among Women Enrolled in Cure Glomerulopathy (CureGN)
Bibliographic record
Abstract
Background: Preeclampsia increases risk for future CKD, possibly through sustained endothelial and podocyte dysfunction. Utilizing CureGN, a longitudinal glomerular disease cohort study, we assessed if complicated pregnancy history was associated with disease progression. Methods: Adult women were classified based on self-reported history of complicated pregnancy (worsening blood pressure, worsening kidney function, increased proteinuria, preeclampsia, eclampsia, or HELLP), pregnancy without these complications, or no pregnancy prior to CureGN enrollment. Linear mixed models assessed associations between complicated pregnancy history and eGFR trajectory as well as UPCR from enrollment. Results: Of 780 women with median follow-up of 32 months, the adjusted eGFR decline [95% CI] was faster in women with a history of complicated pregnancy compared to those without complications or no pregnancy (-2.1 [-2.9, -1.4] vs -0.9 [-1.4, -0.5] and -0.7 [-1.3, -0.1] mL/min/1.73m2 per year, p = 0.01) (Figure). Proteinuria trend did not differ significantly by pregnancy history. Among women with complicated pregnancy (n=124), eGFR slope did not differ significantly by timing of first complicated pregnancy relative to GN diagnosis. Conclusions: A history of complicated pregnancy, occurring at any length of time from GN diagnosis, was associated with faster eGFR decline following CureGN enrollment. A detailed obstetric history may inform counseling regarding disease progression in women with GN. Continued research is warranted to identify biological pathways between complicated pregnancy and progressive glomerular disease.Predicted values of eGFR (95% CI) by pregnancy history from adjusted linear mixed model
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".